本文整理汇总了Python中tensorflow.python.training.basic_session_run_hooks.LoggingTensorHook方法的典型用法代码示例。如果您正苦于以下问题：Python basic_session_run_hooks.LoggingTensorHook方法的具体用法？Python basic_session_run_hooks.LoggingTensorHook怎么用？Python basic_session_run_hooks.LoggingTensorHook使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类tensorflow.python.training.basic_session_run_hooks的用法示例。
在下文中一共展示了basic_session_run_hooks.LoggingTensorHook方法的1个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: _train_model
# 需要导入模块: from tensorflow.python.training import basic_session_run_hooks [as 别名]
# 或者: from tensorflow.python.training.basic_session_run_hooks import LoggingTensorHook [as 别名]
def _train_model(self, input_fn, hooks):
all_hooks = []
self._graph = ops.Graph()
with self._graph.as_default() as g, g.device(self._device_fn):
random_seed.set_random_seed(self._config.tf_random_seed)
global_step = contrib_framework.create_global_step(g)
features, labels = input_fn()
self._check_inputs(features, labels)
model_fn_ops = self._call_legacy_get_train_ops(features, labels)
ops.add_to_collection(ops.GraphKeys.LOSSES, model_fn_ops.loss)
all_hooks.extend([
basic_session_run_hooks.NanTensorHook(model_fn_ops.loss),
basic_session_run_hooks.LoggingTensorHook(
{
'loss': model_fn_ops.loss,
'step': global_step
},
every_n_iter=100)
])
all_hooks.extend(hooks)
scaffold = model_fn_ops.training_scaffold or monitored_session.Scaffold()
if not (scaffold.saver or ops.get_collection(ops.GraphKeys.SAVERS)):
ops.add_to_collection(
ops.GraphKeys.SAVERS,
saver.Saver(
sharded=True,
max_to_keep=self._config.keep_checkpoint_max,
defer_build=True))
chief_hooks = []
if (self._config.save_checkpoints_secs or
self._config.save_checkpoints_steps):
saver_hook_exists = any([
isinstance(h, basic_session_run_hooks.CheckpointSaverHook)
for h in (all_hooks + model_fn_ops.training_hooks + chief_hooks +
model_fn_ops.training_chief_hooks)
])
if not saver_hook_exists:
chief_hooks = [
basic_session_run_hooks.CheckpointSaverHook(
self._model_dir,
save_secs=self._config.save_checkpoints_secs,
save_steps=self._config.save_checkpoints_steps,
scaffold=scaffold)
]
with monitored_session.MonitoredTrainingSession(
master=self._config.master,
is_chief=self._config.is_chief,
checkpoint_dir=self._model_dir,
scaffold=scaffold,
hooks=all_hooks + model_fn_ops.training_hooks,
chief_only_hooks=chief_hooks + model_fn_ops.training_chief_hooks,
save_checkpoint_secs=0, # Saving is handled by a hook.
save_summaries_steps=self._config.save_summary_steps,
config=self.config.tf_config) as mon_sess:
loss = None
while not mon_sess.should_stop():
_, loss = mon_sess.run([model_fn_ops.train_op, model_fn_ops.loss])
summary_io.SummaryWriterCache.clear()
return loss